Prediction of Compressive Strength of SCC-Containing Metakaolin and Rice Husk Ash Using Machine Learning Algorithms

Aggarwal, Sejal; Bhargava, G S; Sihag, Parveen

doi:10.1201/9781003121954-9

Cited by 6 publications

(4 citation statements)

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“…To minimize the need for laboratory experiments as much as possible and provide engineers with more simple techniques and mathematical formulas for predicting experimental results, improved approaches should be used because CS is sensitive to mixture proportions and depends on several parameters. One possible approach in this area would be to use soft computing techniques 14 . To offer the construction industry new methods and techniques, artificial intelligence systems for assessing and forecasting the mechanical properties of cement‐based materials are a popular topic in cement‐based composites research.…”

Section: Introductionmentioning

confidence: 99%

“…One possible approach in this area would be to use soft computing techniques. 14 To offer the construction industry new methods and techniques, artificial intelligence systems for assessing and forecasting the mechanical properties of cement-based materials are a popular topic in cement-based composites research. Additionally, some academics have used machine learning techniques to assess and predict various types of concrete CS.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the compressive strength of strain‐hardening cement‐based composites using soft computing models

Saleh

Jaf

Abdalla

et al. 2023

Structural Concrete

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Strain‐hardening cement‐based composites or engineered cementitious composites (ECC) is concrete produced using randomly distributed short polymer fibers. It is very ductile compared to conventional concrete. Compressive strength (CS) is a critical property used as a quality control tool to evaluate the strength of concrete implemented in the structural provisions and mix designs. Accordingly, to save cost and time for testing, it is essential to provide a predictive model to forecast the CS of the concrete mixtures using machine learning and modeling techniques. In this study, different modeling tools are used to propose analytical models to predict the CS of ECC mixtures, such as linear regression (LR), multi‐expression programming (MEP), artificial neural network (ANN), and Gaussian process regression (GPR). A total of 210 data were collected from the literature and used to train and test the developed model. The fly ash‐to‐cement ratio ranged from 0 to 5.6, water to binder ratio from 0.19 to 0.56, different superplasticizer and fiber content, and curing times of 1 to 180 days. Based on the evaluation of the developed models, the ANN model is superior to other developed models with a high coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and scatter index (SI). The sensitivity analysis of the input parameters' effect on the CS prediction indicates that the curing time and the fly ash‐to‐cement ratio are essential in forecasting ECC's CS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the compressive strength of strain‐hardening cement‐based composites using soft computing models

Saleh

Jaf

Abdalla

et al. 2023

Structural Concrete

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“…Amin et al [31] used a gene expression programming algorithm to investigate the effects of fine and coarse aggregate contents, water-to-binder ratio, compressive strength, and metakaolin content on rapid chloride penetration. Aggarwal et al [32] developed predictive models using random forest, random tree, multilayer perceptron, M5P, and support vector regression algorithms, based on the contents of cement, fine and coarse aggregates, metakaolin, rice husk ash, water, and superplasticizers as input features to predict the 28-day compressive strength of SCC. The models were trained using a dataset of 159 samples.…”

Section: Introductionmentioning

confidence: 99%

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

et al. 2022

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Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.

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“…As CS is sensitive to the mixture's proportions and dependent on various characteristics, engineers should be provided with easier procedures and mathematical formulas to forecast the outcomes of experiments [18]. Soft computing approaches could be considered a viable solution [19]. When mathematical models fail to explain how the problem's primary features are connected adequately, these approaches may be utilized to produce alternatives and solutions for both linear and nonlinear difficulties [20,21].…”

Section: Introductionmentioning

confidence: 99%

Computing Models to Predict the Compressive Strength of Engineered Cementitious Composites (ECC) at Various Mix Proportions

et al. 2022

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Concrete has relatively high compressive strength (resists breaking when squeezed) but significantly lower tensile strength (vulnerable to breaking when pulled apart). The compressive strength is typically controlled by the ratio of water-to-cement when forming the concrete, and tensile strength is increased by additives, typically steel, to create reinforced concrete. In other words, we can say concrete is made up of sand (which is a fine aggregate), ballast (which is a coarse aggregate), cement (which can be referred to as a binder), and water (which is an additive). Highly ductile material engineered cementitious composites (ECC) were developed to address these issues by spreading short polymer fibers randomly throughout a cement-based matrix. It has a high tensile strain capacity of more than 3%, hundreds of times more than conventional concrete. On the other hand, among the other examined qualities, compressive strength (CS) is a critical property. Consequently, developing reliable models to predict an ECC’s compressive strength is crucial for cost, time, and energy savings. It also includes instructions for planning construction projects and calculating the optimal time to remove the formwork. The artificial neural network (ANN), nonlinear model (NLR), linear relationship model (LR), multi-logistic model (MLR), and M5P-tree model were all proposed as alternative models to estimate the CS of ECC mixtures created by fly ash in this research (M5P). To create the models, a large amount of data were gathered and evaluated, totaling roughly 205 mixes. Various mixture proportions, fiber length, diameter, and curing durations were explored as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including determination coefficient (R2), Mean Absolute Error (MAE), Scatter Index (SI), Root Mean Squared Error (RMSE), and Objective (OBJ) value, were utilized. Based on the statistical evaluations, the ANN model performed better in forecasting the CS of ECC mixes incorporating fly ash than other models. This model’s RMSE, MAE, OBJ, and R2 values were 4.55 MPa, 3.46 MPa, 4.39 MPa, and 0.98, respectively. A large database presented in this investigation can be used as the bench mark for future mixture proportions of the ECC. Moreover, the sensitivity analysis showed the contribution of each mixture ingredient on the CS of ECC.

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Prediction of Compressive Strength of SCC-Containing Metakaolin and Rice Husk Ash Using Machine Learning Algorithms

Cited by 6 publications

References 0 publications

Prediction of the compressive strength of strain‐hardening cement‐based composites using soft computing models

Prediction of the compressive strength of strain‐hardening cement‐based composites using soft computing models

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

Computing Models to Predict the Compressive Strength of Engineered Cementitious Composites (ECC) at Various Mix Proportions

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